From 34e82bfce8e5860342cb4f7b3420be3773de1c12 Mon Sep 17 00:00:00 2001
From: jajupmochi <jajupmochi@gmail.com>
Date: Thu, 7 May 2020 17:05:27 +0200
Subject: [PATCH] Add parallel functions to MGE.

---
 gklearn/ged/median/median_graph_estimator.py       |  282 +-
 gklearn/ged/median/test_median_graph_estimator.py  |    8 +-
 gklearn/ged/median/utils.py                        |    2 +
 gklearn/ged/util/util.py                           |    2 +
 gklearn/gedlib/gedlibpy.cpp                        | 2934 ++++++++++----------
 .../gedlibpy.cpython-36m-x86_64-linux-gnu.so       |  Bin 33390352 -> 33390352 bytes
 gklearn/gedlib/gedlibpy.pyx                        |   10 +-
 gklearn/preimage/median_preimage_generator.py      |    8 +-
 8 files changed, 1708 insertions(+), 1538 deletions(-)

diff --git a/gklearn/ged/median/median_graph_estimator.py b/gklearn/ged/median/median_graph_estimator.py
index df21601..9e0db71 100644
--- a/gklearn/ged/median/median_graph_estimator.py
+++ b/gklearn/ged/median/median_graph_estimator.py
@@ -13,6 +13,9 @@ import time
 from tqdm import tqdm
 import sys
 import networkx as nx
+import multiprocessing
+from multiprocessing import Pool
+from functools import partial
 
 
 class MedianGraphEstimator(object): # @todo: differ dummy_node from undifined node?
@@ -47,6 +50,7 @@ class MedianGraphEstimator(object): # @todo: differ dummy_node from undifined no
 		self.__desired_num_random_inits = 10
 		self.__use_real_randomness = True
 		self.__seed = 0
+		self.__parallel = True
 		self.__update_order = True
 		self.__refine = True
 		self.__time_limit_in_sec = 0
@@ -125,6 +129,16 @@ class MedianGraphEstimator(object): # @todo: differ dummy_node from undifined no
 	
 				else:
 					raise Exception('Invalid argument "' + opt_val  + '" for option stdout. Usage: options = "[--stdout 0|1|2] [...]"')
+
+			elif opt_name == 'parallel':
+				if opt_val == 'TRUE':
+					self.__parallel = True
+	
+				elif opt_val == 'FALSE':
+					self.__parallel = False
+	
+				else:
+					raise Exception('Invalid argument "' + opt_val  + '" for option parallel. Usage: options = "[--parallel TRUE|FALSE] [...]"')
 	
 			elif opt_name == 'update-order':
 				if opt_val == 'TRUE':
@@ -312,7 +326,6 @@ class MedianGraphEstimator(object): # @todo: differ dummy_node from undifined no
 #		print(graphs[0].nodes(data=True))
 #		print(graphs[0].edges(data=True))
 #		print(nx.adjacency_matrix(graphs[0]))
-
 			
 		# Construct initial medians.
 		medians = []
@@ -356,30 +369,12 @@ class MedianGraphEstimator(object): # @todo: differ dummy_node from undifined no
 			self.__ged_env.load_nx_graph(median, gen_median_id)
 			self.__ged_env.init(self.__ged_env.get_init_type())
 			
-			# Print information about current iteration.
-			if self.__print_to_stdout == 2:
-				progress = tqdm(desc='Computing initial node maps', total=len(graph_ids), file=sys.stdout)
-				
 			# Compute node maps and sum of distances for initial median.
-			self.__sum_of_distances = 0
-			self.__node_maps_from_median.clear()
-			for graph_id in graph_ids:
-				self.__ged_env.run_method(gen_median_id, graph_id)
-				self.__node_maps_from_median[graph_id] = self.__ged_env.get_node_map(gen_median_id, graph_id)
-# 				print(self.__node_maps_from_median[graph_id])
-				self.__sum_of_distances += self.__node_maps_from_median[graph_id].induced_cost()
-# 				print(self.__sum_of_distances)
-				# Print information about current iteration.
-				if self.__print_to_stdout == 2:
-					progress.update(1)
-					
+			self.__compute_init_node_maps(graph_ids, gen_median_id)
+			
 			self.__best_init_sum_of_distances = min(self.__best_init_sum_of_distances, self.__sum_of_distances)
 			self.__ged_env.load_nx_graph(median, set_median_id)
 # 			print(self.__best_init_sum_of_distances)
-			
-			# Print information about current iteration.
-			if self.__print_to_stdout == 2:
-				print('\n')
 				
 			# Run block gradient descent from initial median.
 			converged = False
@@ -434,7 +429,7 @@ class MedianGraphEstimator(object): # @todo: differ dummy_node from undifined no
 # 					print(self.__node_maps_from_median[graph_id].induced_cost())
 # 					xxx = self.__node_maps_from_median[graph_id]					   
 					self.__ged_env.compute_induced_cost(gen_median_id, graph_id, self.__node_maps_from_median[graph_id])
-#					print('---------------------------------------')
+# 					print('---------------------------------------')
 # 					print(self.__node_maps_from_median[graph_id].induced_cost())
 					# @todo:!!!!!!!!!!!!!!!!!!!!!!!!!!!!This value is a slight different from the c++ program, which might be a bug! Use it very carefully!
 					
@@ -637,6 +632,7 @@ class MedianGraphEstimator(object): # @todo: differ dummy_node from undifined no
 		self.__desired_num_random_inits = 10
 		self.__use_real_randomness = True
 		self.__seed = 0
+		self.__parallel = True
 		self.__update_order = True
 		self.__refine = True
 		self.__time_limit_in_sec = 0
@@ -682,35 +678,123 @@ class MedianGraphEstimator(object): # @todo: differ dummy_node from undifined no
 	def __compute_medoid(self, graph_ids, timer, initial_medians):
 		# Use method selected for initialization phase.
 		self.__ged_env.set_method(self.__init_method, self.__init_options)
-		
-		# Print information about current iteration.
-		if self.__print_to_stdout == 2:
-			progress = tqdm(desc='Computing medoid', total=len(graph_ids), file=sys.stdout)
-			
+					
 		# Compute the medoid.
-		medoid_id = graph_ids[0]
-		best_sum_of_distances = np.inf
-		for g_id in graph_ids:
-			if timer.expired():
-				self.__state = AlgorithmState.CALLED
-				break
-			sum_of_distances = 0
-			for h_id in graph_ids:
-				self.__ged_env.run_method(g_id, h_id)
-				sum_of_distances += self.__ged_env.get_upper_bound(g_id, h_id)
-			if sum_of_distances < best_sum_of_distances:
-				best_sum_of_distances = sum_of_distances
-				medoid_id = g_id
-				
+		if self.__parallel:
+			# @todo: notice when parallel self.__ged_env is not modified.
+			sum_of_distances_list = [np.inf] * len(graph_ids)
+			len_itr = len(graph_ids)
+			itr = zip(graph_ids, range(0, len(graph_ids)))
+			n_jobs = multiprocessing.cpu_count()
+			if len_itr < 100 * n_jobs:
+				chunksize = int(len_itr / n_jobs) + 1
+			else:
+				chunksize = 100
+			def init_worker(ged_env_toshare):
+				global G_ged_env
+				G_ged_env = ged_env_toshare
+			do_fun = partial(_compute_medoid_parallel, graph_ids)
+			pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(self.__ged_env,))
+			if self.__print_to_stdout == 2:
+				iterator = tqdm(pool.imap_unordered(do_fun, itr, chunksize),
+							desc='Computing medoid', file=sys.stdout)
+			else:
+				iterator = pool.imap_unordered(do_fun, itr, chunksize)
+			for i, dis in iterator:
+				sum_of_distances_list[i] = dis
+			pool.close()
+			pool.join()
+			
+			medoid_id = np.argmin(sum_of_distances_list)
+			best_sum_of_distances = sum_of_distances_list[medoid_id]
+			
+			initial_medians.append(self.__ged_env.get_nx_graph(medoid_id, True, True, False)) # @todo
+
+		else:
 			# Print information about current iteration.
 			if self.__print_to_stdout == 2:
-				progress.update(1)
-		initial_medians.append(self.__ged_env.get_nx_graph(medoid_id, True, True, False)) # @todo
+				progress = tqdm(desc='Computing medoid', total=len(graph_ids), file=sys.stdout)
 		
-		# Print information about current iteration.
-		if self.__print_to_stdout == 2:
-			print('\n')
+			medoid_id = graph_ids[0]
+			best_sum_of_distances = np.inf
+			for g_id in graph_ids:
+				if timer.expired():
+					self.__state = AlgorithmState.CALLED
+					break
+				sum_of_distances = 0
+				for h_id in graph_ids:
+					self.__ged_env.run_method(g_id, h_id)
+					sum_of_distances += self.__ged_env.get_upper_bound(g_id, h_id)
+				if sum_of_distances < best_sum_of_distances:
+					best_sum_of_distances = sum_of_distances
+					medoid_id = g_id
+					
+				# Print information about current iteration.
+				if self.__print_to_stdout == 2:
+					progress.update(1)
+					
+			initial_medians.append(self.__ged_env.get_nx_graph(medoid_id, True, True, False)) # @todo
+			
+			# Print information about current iteration.
+			if self.__print_to_stdout == 2:
+				print('\n')
+			
+			
+	def __compute_init_node_maps(self, graph_ids, gen_median_id):
+		# Compute node maps and sum of distances for initial median.
+		if self.__parallel:
+			# @todo: notice when parallel self.__ged_env is not modified.
+			self.__sum_of_distances = 0
+			self.__node_maps_from_median.clear()
+			sum_of_distances_list = [0] * len(graph_ids)
+			
+			len_itr = len(graph_ids)
+			itr = graph_ids
+			n_jobs = multiprocessing.cpu_count()
+			if len_itr < 100 * n_jobs:
+				chunksize = int(len_itr / n_jobs) + 1
+			else:
+				chunksize = 100
+			def init_worker(ged_env_toshare):
+				global G_ged_env
+				G_ged_env = ged_env_toshare
+			do_fun = partial(_compute_init_node_maps_parallel, gen_median_id)
+			pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(self.__ged_env,))
+			if self.__print_to_stdout == 2:
+				iterator = tqdm(pool.imap_unordered(do_fun, itr, chunksize),
+							desc='Computing initial node maps', file=sys.stdout)
+			else:
+				iterator = pool.imap_unordered(do_fun, itr, chunksize)
+			for g_id, sod, node_maps in iterator:
+				sum_of_distances_list[g_id] = sod
+				self.__node_maps_from_median[g_id] = node_maps
+			pool.close()
+			pool.join()
 			
+			self.__sum_of_distances = np.sum(sum_of_distances_list)
+# 			xxx = self.__node_maps_from_median
+			
+		else:
+			# Print information about current iteration.
+			if self.__print_to_stdout == 2:
+				progress = tqdm(desc='Computing initial node maps', total=len(graph_ids), file=sys.stdout)
+				
+			self.__sum_of_distances = 0
+			self.__node_maps_from_median.clear()
+			for graph_id in graph_ids:
+				self.__ged_env.run_method(gen_median_id, graph_id)
+				self.__node_maps_from_median[graph_id] = self.__ged_env.get_node_map(gen_median_id, graph_id)
+	# 				print(self.__node_maps_from_median[graph_id])
+				self.__sum_of_distances += self.__node_maps_from_median[graph_id].induced_cost()
+	# 				print(self.__sum_of_distances)
+				# Print information about current iteration.
+				if self.__print_to_stdout == 2:
+					progress.update(1)
+			
+			# Print information about current iteration.
+			if self.__print_to_stdout == 2:
+				print('\n')
+
 		
 	def __termination_criterion_met(self, converged, timer, itr, itrs_without_update):
 		if timer.expired() or (itr >= self.__max_itrs if self.__max_itrs >= 0 else False):
@@ -816,26 +900,57 @@ class MedianGraphEstimator(object): # @todo: differ dummy_node from undifined no
 
 
 	def __update_node_maps(self):
-		# Print information about current iteration.
-		if self.__print_to_stdout == 2:
-			progress = tqdm(desc='Updating node maps', total=len(self.__node_maps_from_median), file=sys.stdout)
-			
 		# Update the node maps.
-		node_maps_were_modified = False
-		for graph_id, node_map in self.__node_maps_from_median.items():
-			self.__ged_env.run_method(self.__median_id, graph_id)
-			if self.__ged_env.get_upper_bound(self.__median_id, graph_id) < node_map.induced_cost() - self.__epsilon:
-# 				xxx = self.__node_maps_from_median[graph_id]
-				self.__node_maps_from_median[graph_id] = self.__ged_env.get_node_map(self.__median_id, graph_id)
-# 				yyy = self.__node_maps_from_median[graph_id]
-				node_maps_were_modified = True
+		if self.__parallel:
+			# @todo: notice when parallel self.__ged_env is not modified.
+			node_maps_were_modified = False
+# 			xxx = self.__node_maps_from_median.copy()
+			
+			len_itr = len(self.__node_maps_from_median)
+			itr = [item for item in self.__node_maps_from_median.items()]
+			n_jobs = multiprocessing.cpu_count()
+			if len_itr < 100 * n_jobs:
+				chunksize = int(len_itr / n_jobs) + 1
+			else:
+				chunksize = 100
+			def init_worker(ged_env_toshare):
+				global G_ged_env
+				G_ged_env = ged_env_toshare
+			do_fun = partial(_update_node_maps_parallel, self.__median_id, self.__epsilon)
+			pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(self.__ged_env,))
+			if self.__print_to_stdout == 2:
+				iterator = tqdm(pool.imap_unordered(do_fun, itr, chunksize),
+							desc='Updating node maps', file=sys.stdout)
+			else:
+				iterator = pool.imap_unordered(do_fun, itr, chunksize)
+			for g_id, node_map, nm_modified in iterator:
+				self.__node_maps_from_median[g_id] = node_map
+				if nm_modified:
+					node_maps_were_modified = True
+			pool.close()
+			pool.join()
+# 			yyy = self.__node_maps_from_median.copy()
+
+		else:
 			# Print information about current iteration.
 			if self.__print_to_stdout == 2:
-				progress.update(1)
-			
-		# Print information about current iteration.
-		if self.__print_to_stdout == 2:
-			print('\n')
+				progress = tqdm(desc='Updating node maps', total=len(self.__node_maps_from_median), file=sys.stdout)
+				
+			node_maps_were_modified = False
+			for graph_id, node_map in self.__node_maps_from_median.items():
+				self.__ged_env.run_method(self.__median_id, graph_id)
+				if self.__ged_env.get_upper_bound(self.__median_id, graph_id) < node_map.induced_cost() - self.__epsilon:
+	# 				xxx = self.__node_maps_from_median[graph_id]
+					self.__node_maps_from_median[graph_id] = self.__ged_env.get_node_map(self.__median_id, graph_id)
+	# 				yyy = self.__node_maps_from_median[graph_id]
+					node_maps_were_modified = True
+				# Print information about current iteration.
+				if self.__print_to_stdout == 2:
+					progress.update(1)
+				
+			# Print information about current iteration.
+			if self.__print_to_stdout == 2:
+				print('\n')
 			
 		# Return true if the node maps were modified.
 		return node_maps_were_modified
@@ -1230,7 +1345,8 @@ class MedianGraphEstimator(object): # @todo: differ dummy_node from undifined no
 	
 	def __add_node_to_median(self, best_config, best_label, median):
 		# Update the median.
-		median.add_node(nx.number_of_nodes(median), **best_label)
+		nb_nodes_median = nx.number_of_nodes(median)
+		median.add_node(nb_nodes_median, **best_label)
 		
 		# Update the node maps.
 		for graph_id, node_map in self.__node_maps_from_median.items():
@@ -1494,4 +1610,40 @@ class MedianGraphEstimator(object): # @todo: differ dummy_node from undifined no
 #			median_label = {}
 #			for key, val in median.items():
 #				median_label[key] = str(val)
-#			return median_label
\ No newline at end of file
+#			return median_label
+
+
+def _compute_medoid_parallel(graph_ids, itr):
+	g_id = itr[0]
+	i = itr[1]
+	# @todo: timer not considered here.
+# 			if timer.expired():
+# 				self.__state = AlgorithmState.CALLED
+# 				break
+	sum_of_distances = 0
+	for h_id in graph_ids:
+		G_ged_env.run_method(g_id, h_id)
+		sum_of_distances += G_ged_env.get_upper_bound(g_id, h_id)
+	return i, sum_of_distances
+
+
+def _compute_init_node_maps_parallel(gen_median_id, itr):
+	graph_id = itr
+	G_ged_env.run_method(gen_median_id, graph_id)
+	node_maps_from_median = G_ged_env.get_node_map(gen_median_id, graph_id)
+# 				print(self.__node_maps_from_median[graph_id])
+	sum_of_distance = node_maps_from_median.induced_cost()
+# 				print(self.__sum_of_distances)
+	return graph_id, sum_of_distance, node_maps_from_median
+
+
+def _update_node_maps_parallel(median_id, epsilon, itr):
+	graph_id = itr[0]
+	node_map = itr[1]
+
+	node_maps_were_modified = False
+	G_ged_env.run_method(median_id, graph_id)
+	if G_ged_env.get_upper_bound(median_id, graph_id) < node_map.induced_cost() - epsilon:
+		node_map = G_ged_env.get_node_map(median_id, graph_id)
+		node_maps_were_modified = True
+	return graph_id, node_map, node_maps_were_modified
\ No newline at end of file
diff --git a/gklearn/ged/median/test_median_graph_estimator.py b/gklearn/ged/median/test_median_graph_estimator.py
index 7497bab..60bce83 100644
--- a/gklearn/ged/median/test_median_graph_estimator.py
+++ b/gklearn/ged/median/test_median_graph_estimator.py
@@ -53,7 +53,7 @@ def test_median_graph_estimator():
 	mge.set_refine_method(algo, '--threads ' + str(threads) + ' --initial-solutions ' + str(initial_solutions) + ' --ratio-runs-from-initial-solutions 1')
 	
 	mge_options = '--time-limit ' + str(time_limit) + ' --stdout 2 --init-type ' + init_type
-	mge_options += ' --random-inits ' + str(num_inits) + ' --seed ' + '1'  + ' --update-order TRUE --refine FALSE --randomness PSEUDO '# @todo: std::to_string(rng())
+	mge_options += ' --random-inits ' + str(num_inits) + ' --seed ' + '1'  + ' --update-order TRUE --refine FALSE --randomness PSEUDO  --parallel TRUE '# @todo: std::to_string(rng())
 	
 	# Select the GED algorithm.
 	algo_options = '--threads ' + str(threads) + algo_options_suffix
@@ -127,7 +127,7 @@ def test_median_graph_estimator_symb():
 	mge.set_refine_method(algo, '--threads ' + str(threads) + ' --initial-solutions ' + str(initial_solutions) + ' --ratio-runs-from-initial-solutions 1')
 	
 	mge_options = '--time-limit ' + str(time_limit) + ' --stdout 2 --init-type ' + init_type
-	mge_options += ' --random-inits ' + str(num_inits) + ' --seed ' + '1'  + ' --update-order TRUE --refine FALSE'# @todo: std::to_string(rng())
+	mge_options += ' --random-inits ' + str(num_inits) + ' --seed ' + '1'  + ' --update-order TRUE --refine FALSE --randomness PSEUDO --parallel TRUE '# @todo: std::to_string(rng())
 	
 	# Select the GED algorithm.
 	algo_options = '--threads ' + str(threads) + algo_options_suffix
@@ -155,5 +155,5 @@ def test_median_graph_estimator_symb():
 
 
 if __name__ == '__main__':
- 	set_median, gen_median = test_median_graph_estimator()
- 	# set_median, gen_median = test_median_graph_estimator_symb()
\ No newline at end of file
+ 	# set_median, gen_median = test_median_graph_estimator()
+ 	set_median, gen_median = test_median_graph_estimator_symb()
\ No newline at end of file
diff --git a/gklearn/ged/median/utils.py b/gklearn/ged/median/utils.py
index 908cb11..5c4c52f 100644
--- a/gklearn/ged/median/utils.py
+++ b/gklearn/ged/median/utils.py
@@ -30,6 +30,8 @@ def mge_options_to_string(options):
 			opt_str += '--randomness ' + str(val) + ' '
 		elif key == 'verbose':
 			opt_str += '--stdout ' + str(val) + ' '
+		elif key == 'parallel':
+			opt_str += '--parallel ' + ('TRUE' if val else 'FALSE') + ' '
 		elif key == 'update_order':
 			opt_str += '--update-order ' + ('TRUE' if val else 'FALSE') + ' '
 		elif key == 'refine':
diff --git a/gklearn/ged/util/util.py b/gklearn/ged/util/util.py
index a18b0cb..d72c2e6 100644
--- a/gklearn/ged/util/util.py
+++ b/gklearn/ged/util/util.py
@@ -54,6 +54,8 @@ def compute_geds(graphs, options={}, parallel=False):
 		ged_env.add_nx_graph(g, '')
 	listID = ged_env.get_all_graph_ids()	
 	ged_env.init()
+	if parallel:
+		options['threads'] = 1
 	ged_env.set_method(options['method'], ged_options_to_string(options))
 	ged_env.init_method()
 
diff --git a/gklearn/gedlib/gedlibpy.cpp b/gklearn/gedlib/gedlibpy.cpp
index 58aa1fd..18e7cd8 100644
--- a/gklearn/gedlib/gedlibpy.cpp
+++ b/gklearn/gedlib/gedlibpy.cpp
@@ -1173,9 +1173,9 @@ typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;
  */
 typedef npy_cdouble __pyx_t_5numpy_complex_t;
 
-/* "gedlibpy.pyx":180
- * 
+/* "gedlibpy.pyx":182
  * 
+ * # @cython.auto_pickle(True)
  * cdef class GEDEnv:             # <<<<<<<<<<<<<<
  * 	"""Cython wrapper class for C++ class PyGEDEnv
  * 	"""
@@ -2155,6 +2155,8 @@ static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, cha
 static PyTypeObject *__pyx_ptype_7cpython_5array_array = 0;
 static CYTHON_INLINE int __pyx_f_7cpython_5array_extend_buffer(arrayobject *, char *, Py_ssize_t); /*proto*/
 
+/* Module declarations from 'cython' */
+
 /* Module declarations from 'gedlibpy' */
 static PyTypeObject *__pyx_ptype_8gedlibpy_GEDEnv = 0;
 static CYTHON_INLINE PyObject *__pyx_convert_PyObject_string_to_py_std__in_string(std::string const &); /*proto*/
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  * 		print("Loading graphs in progress...")
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  * 		print("Loading graphs in progress...")
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  * 		print("Loading graphs in progress...")
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  * 		listID = self.graph_ids()
  * 		print("Graphs loaded ! ")
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@@ -10119,13 +10121,13 @@ static PyObject *__pyx_pf_8gedlibpy_6GEDEnv_90compute_edit_distance_on_nx_graphs
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  * 		for graph in dataset :
  * 			self.add_nx_graph(graph, classes)
  * 		listID = self.graph_ids()             # <<<<<<<<<<<<<<
  * 		print("Graphs loaded ! ")
  * 		print("Number of graphs = " + str(listID[1]))
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-  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_graph_ids); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 980, __pyx_L1_error)
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  * 		print("Number of graphs = " + str(listID[1]))
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  * 		self.set_edit_cost(edit_cost)             # <<<<<<<<<<<<<<
  * 		print("Initialization in progress...")
  * 		self.init(init_option)
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@@ -10232,30 +10234,30 @@ static PyObject *__pyx_pf_8gedlibpy_6GEDEnv_90compute_edit_distance_on_nx_graphs
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-  /* "gedlibpy.pyx":985
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  * 		print("Initialization in progress...")             # <<<<<<<<<<<<<<
  * 		self.init(init_option)
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-  /* "gedlibpy.pyx":986
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  * 		self.set_edit_cost(edit_cost)
  * 		print("Initialization in progress...")
  * 		self.init(init_option)             # <<<<<<<<<<<<<<
  * 		print("Initialization terminated !")
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-  __pyx_t_2 = PyList_New(0); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 992, __pyx_L1_error)
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-  __pyx_t_1 = PyList_New(0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 993, __pyx_L1_error)
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  * 				self.run_method(g, h)
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  * 				resMapping[g][h] = self.get_node_map(g, h)             # <<<<<<<<<<<<<<
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  * 		print("Finish ! The return contains edit distances and NodeMap but you can check the result with graphs'ID until you restart the environment")
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-      /* "gedlibpy.pyx":996
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  * 		for g in range(listID[0], listID[1]) :
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  * 			for h in range(listID[0], listID[1]) :             # <<<<<<<<<<<<<<
@@ -10737,7 +10739,7 @@ static PyObject *__pyx_pf_8gedlibpy_6GEDEnv_90compute_edit_distance_on_nx_graphs
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-    /* "gedlibpy.pyx":994
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  * 		resDistance = [[]]
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  * 				resMapping[g][h] = self.get_node_map(g, h)
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  * 		print("Finish ! The return contains edit distances and NodeMap but you can check the result with graphs'ID until you restart the environment")             # <<<<<<<<<<<<<<
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-  /* "gedlibpy.pyx":1003
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  * 		print("Finish ! The return contains edit distances and NodeMap but you can check the result with graphs'ID until you restart the environment")
  * 		return resDistance, resMapping             # <<<<<<<<<<<<<<
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-  /* "gedlibpy.pyx":950
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@@ -10862,19 +10864,19 @@ static PyObject *__pyx_pw_8gedlibpy_6GEDEnv_93compute_edit_distance_on_GXl_graph
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  * 		print("Loading graphs in progress...")
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-    __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_restart_env); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 1031, __pyx_L1_error)
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@@ -11011,25 +11013,25 @@ static PyObject *__pyx_pf_8gedlibpy_6GEDEnv_92compute_edit_distance_on_GXl_graph
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  * 		print("Loading graphs in progress...")             # <<<<<<<<<<<<<<
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  * 		print("Loading graphs in progress...")
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  * 		listID = self.graph_ids()
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-  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_load_GXL_graphs); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 1034, __pyx_L1_error)
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@@ -11046,7 +11048,7 @@ static PyObject *__pyx_pf_8gedlibpy_6GEDEnv_92compute_edit_distance_on_GXl_graph
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@@ -11054,13 +11056,13 @@ static PyObject *__pyx_pf_8gedlibpy_6GEDEnv_92compute_edit_distance_on_GXl_graph
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  * 		self.load_GXL_graphs(path_folder, path_XML)
  * 		listID = self.graph_ids()             # <<<<<<<<<<<<<<
  * 		print("Graphs loaded ! ")
  * 		print("Number of graphs = " + str(listID[1]))
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-  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_graph_ids); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 1035, __pyx_L1_error)
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  * 		listID = self.graph_ids()
  * 		print("Graphs loaded ! ")             # <<<<<<<<<<<<<<
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  * 		print("Graphs loaded ! ")
  * 		print("Number of graphs = " + str(listID[1]))             # <<<<<<<<<<<<<<
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-  /* "gedlibpy.pyx":1039
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  * 		print("Number of graphs = " + str(listID[1]))
  * 
  * 		self.set_edit_cost(edit_cost)             # <<<<<<<<<<<<<<
  * 		print("Initialization in progress...")
  * 		self.init(init_option)
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-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_set_edit_cost); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1039, __pyx_L1_error)
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@@ -11157,30 +11159,30 @@ static PyObject *__pyx_pf_8gedlibpy_6GEDEnv_92compute_edit_distance_on_GXl_graph
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-  /* "gedlibpy.pyx":1040
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  * 		self.set_edit_cost(edit_cost)
  * 		print("Initialization in progress...")             # <<<<<<<<<<<<<<
  * 		self.init(init_option)
  * 		print("Initialization terminated !")
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-  /* "gedlibpy.pyx":1041
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  * 		self.set_edit_cost(edit_cost)
  * 		print("Initialization in progress...")
  * 		self.init(init_option)             # <<<<<<<<<<<<<<
  * 		print("Initialization terminated !")
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-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_init); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1041, __pyx_L1_error)
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  * 			for h in range(listID[0], listID[1]) :
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+      /* "gedlibpy.pyx":1058
  * 		for g in range(listID[0], listID[1]) :
  * 			print("Computation between graph " + str(g) + " with all the others including himself.")
  * 			for h in range(listID[0], listID[1]) :             # <<<<<<<<<<<<<<
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-  /* "gedlibpy.pyx":1057
+  /* "gedlibpy.pyx":1065
  * 		#return res
  * 
  * 		print ("Finish ! You can check the result with each ID of graphs ! There are in the return")             # <<<<<<<<<<<<<<
  * 		print ("Please don't restart the environment or recall this function, you will lose your results !")
  * 		return listID
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+  /* "gedlibpy.pyx":1066
  * 
  * 		print ("Finish ! You can check the result with each ID of graphs ! There are in the return")
  * 		print ("Please don't restart the environment or recall this function, you will lose your results !")             # <<<<<<<<<<<<<<
  * 		return listID
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+  /* "gedlibpy.pyx":1067
  * 		print ("Finish ! You can check the result with each ID of graphs ! There are in the return")
  * 		print ("Please don't restart the environment or recall this function, you will lose your results !")
  * 		return listID             # <<<<<<<<<<<<<<
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   __pyx_r = __pyx_v_listID;
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  * 
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  * 		nb_nodes = self.get_graph_num_nodes(graph_id)
  * 		original_node_ids = self.get_original_node_ids(graph_id)
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+  __pyx_builtin_range = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_range) __PYX_ERR(0, 758, __pyx_L1_error)
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-  /* "gedlibpy.pyx":977
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  * 			self.restart_env()
  * 
  * 		print("Loading graphs in progress...")             # <<<<<<<<<<<<<<
  * 		for graph in dataset :
  * 			self.add_nx_graph(graph, classes)
  */
-  __pyx_tuple__4 = PyTuple_Pack(1, __pyx_kp_u_Loading_graphs_in_progress); if (unlikely(!__pyx_tuple__4)) __PYX_ERR(0, 977, __pyx_L1_error)
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-  /* "gedlibpy.pyx":981
+  /* "gedlibpy.pyx":989
  * 			self.add_nx_graph(graph, classes)
  * 		listID = self.graph_ids()
  * 		print("Graphs loaded ! ")             # <<<<<<<<<<<<<<
  * 		print("Number of graphs = " + str(listID[1]))
  * 
  */
-  __pyx_tuple__5 = PyTuple_Pack(1, __pyx_kp_u_Graphs_loaded); if (unlikely(!__pyx_tuple__5)) __PYX_ERR(0, 981, __pyx_L1_error)
+  __pyx_tuple__5 = PyTuple_Pack(1, __pyx_kp_u_Graphs_loaded); if (unlikely(!__pyx_tuple__5)) __PYX_ERR(0, 989, __pyx_L1_error)
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   __Pyx_GIVEREF(__pyx_tuple__5);
 
-  /* "gedlibpy.pyx":985
+  /* "gedlibpy.pyx":993
  * 
  * 		self.set_edit_cost(edit_cost)
  * 		print("Initialization in progress...")             # <<<<<<<<<<<<<<
  * 		self.init(init_option)
  * 		print("Initialization terminated !")
  */
-  __pyx_tuple__6 = PyTuple_Pack(1, __pyx_kp_u_Initialization_in_progress); if (unlikely(!__pyx_tuple__6)) __PYX_ERR(0, 985, __pyx_L1_error)
+  __pyx_tuple__6 = PyTuple_Pack(1, __pyx_kp_u_Initialization_in_progress); if (unlikely(!__pyx_tuple__6)) __PYX_ERR(0, 993, __pyx_L1_error)
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   __Pyx_GIVEREF(__pyx_tuple__6);
 
-  /* "gedlibpy.pyx":987
+  /* "gedlibpy.pyx":995
  * 		print("Initialization in progress...")
  * 		self.init(init_option)
  * 		print("Initialization terminated !")             # <<<<<<<<<<<<<<
  * 
  * 		self.set_method(method, options)
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-  __pyx_tuple__7 = PyTuple_Pack(1, __pyx_kp_u_Initialization_terminated); if (unlikely(!__pyx_tuple__7)) __PYX_ERR(0, 987, __pyx_L1_error)
+  __pyx_tuple__7 = PyTuple_Pack(1, __pyx_kp_u_Initialization_terminated); if (unlikely(!__pyx_tuple__7)) __PYX_ERR(0, 995, __pyx_L1_error)
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   __Pyx_GIVEREF(__pyx_tuple__7);
 
-  /* "gedlibpy.pyx":1002
+  /* "gedlibpy.pyx":1010
  * 				resMapping[g][h] = self.get_node_map(g, h)
  * 
  * 		print("Finish ! The return contains edit distances and NodeMap but you can check the result with graphs'ID until you restart the environment")             # <<<<<<<<<<<<<<
  * 		return resDistance, resMapping
  * 
  */
-  __pyx_tuple__8 = PyTuple_Pack(1, __pyx_kp_u_Finish_The_return_contains_edit); if (unlikely(!__pyx_tuple__8)) __PYX_ERR(0, 1002, __pyx_L1_error)
+  __pyx_tuple__8 = PyTuple_Pack(1, __pyx_kp_u_Finish_The_return_contains_edit); if (unlikely(!__pyx_tuple__8)) __PYX_ERR(0, 1010, __pyx_L1_error)
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   __Pyx_GIVEREF(__pyx_tuple__8);
 
-  /* "gedlibpy.pyx":1057
+  /* "gedlibpy.pyx":1065
  * 		#return res
  * 
  * 		print ("Finish ! You can check the result with each ID of graphs ! There are in the return")             # <<<<<<<<<<<<<<
  * 		print ("Please don't restart the environment or recall this function, you will lose your results !")
  * 		return listID
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-  __pyx_tuple__9 = PyTuple_Pack(1, __pyx_kp_u_Finish_You_can_check_the_result); if (unlikely(!__pyx_tuple__9)) __PYX_ERR(0, 1057, __pyx_L1_error)
+  __pyx_tuple__9 = PyTuple_Pack(1, __pyx_kp_u_Finish_You_can_check_the_result); if (unlikely(!__pyx_tuple__9)) __PYX_ERR(0, 1065, __pyx_L1_error)
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   __Pyx_GIVEREF(__pyx_tuple__9);
 
-  /* "gedlibpy.pyx":1058
+  /* "gedlibpy.pyx":1066
  * 
  * 		print ("Finish ! You can check the result with each ID of graphs ! There are in the return")
  * 		print ("Please don't restart the environment or recall this function, you will lose your results !")             # <<<<<<<<<<<<<<
  * 		return listID
  * 
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-  __pyx_tuple__10 = PyTuple_Pack(1, __pyx_kp_u_Please_don_t_restart_the_environ); if (unlikely(!__pyx_tuple__10)) __PYX_ERR(0, 1058, __pyx_L1_error)
+  __pyx_tuple__10 = PyTuple_Pack(1, __pyx_kp_u_Please_don_t_restart_the_environ); if (unlikely(!__pyx_tuple__10)) __PYX_ERR(0, 1066, __pyx_L1_error)
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@@ -21339,122 +21341,122 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) {
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   __Pyx_GIVEREF(__pyx_tuple__19);
 
-  /* "gedlibpy.pyx":128
+  /* "gedlibpy.pyx":129
  * 
  * 
  * def get_edit_cost_options() :             # <<<<<<<<<<<<<<
  * 	"""
  * 		Searchs the differents edit cost functions and returns the result.
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-  __pyx_tuple__21 = PyTuple_Pack(1, __pyx_n_s_option); if (unlikely(!__pyx_tuple__21)) __PYX_ERR(0, 128, __pyx_L1_error)
+  __pyx_tuple__21 = PyTuple_Pack(1, __pyx_n_s_option); if (unlikely(!__pyx_tuple__21)) __PYX_ERR(0, 129, __pyx_L1_error)
   __Pyx_GOTREF(__pyx_tuple__21);
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-  /* "gedlibpy.pyx":142
+  /* "gedlibpy.pyx":143
  * 
  * 
  * def get_method_options() :             # <<<<<<<<<<<<<<
  * 	"""
  * 		Searchs the differents method for edit distance computation between graphs and returns the result.
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-  __pyx_tuple__23 = PyTuple_Pack(1, __pyx_n_s_option); if (unlikely(!__pyx_tuple__23)) __PYX_ERR(0, 142, __pyx_L1_error)
+  __pyx_tuple__23 = PyTuple_Pack(1, __pyx_n_s_option); if (unlikely(!__pyx_tuple__23)) __PYX_ERR(0, 143, __pyx_L1_error)
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-  /* "gedlibpy.pyx":155
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diff --git a/gklearn/gedlib/gedlibpy.pyx b/gklearn/gedlib/gedlibpy.pyx
index 54f24b0..7fb7e84 100644
--- a/gklearn/gedlib/gedlibpy.pyx
+++ b/gklearn/gedlib/gedlibpy.pyx
@@ -112,6 +112,7 @@ cdef extern from "src/GedLibBind.hpp" namespace "pyged":
 ##CYTHON WRAPPER INTERFACES##
 #############################
 
+# import cython
 import numpy as np
 import networkx as nx
 from gklearn.ged.env import NodeMap
@@ -177,14 +178,16 @@ def get_dummy_node() :
 	return getDummyNode()
 	
 
+# @cython.auto_pickle(True)
 cdef class GEDEnv:
 	"""Cython wrapper class for C++ class PyGEDEnv
 	"""
-	# cdef PyGEDEnv c_env  # Hold a C++ instance which we're wrapping
+# 	cdef PyGEDEnv c_env  # Hold a C++ instance which we're wrapping
 	cdef PyGEDEnv* c_env  # hold a pointer to the C++ instance which we're wrapping
 
 
 	def __cinit__(self):
+# 		self.c_env = PyGEDEnv()
 		self.c_env = new PyGEDEnv()
 		
 	
@@ -192,6 +195,11 @@ cdef class GEDEnv:
 		del self.c_env
 
 
+# 	def __reduce__(self):
+# # 		return GEDEnv, (self.c_env,)
+# 		return GEDEnv, tuple()
+
+
 	def is_initialized(self) :
 		"""
 			Checks and returns if the computation environment is initialized or not.
diff --git a/gklearn/preimage/median_preimage_generator.py b/gklearn/preimage/median_preimage_generator.py
index fa1f4db..8753292 100644
--- a/gklearn/preimage/median_preimage_generator.py
+++ b/gklearn/preimage/median_preimage_generator.py
@@ -669,6 +669,7 @@ class MedianPreimageGenerator(PreimageGenerator):
 		options = self.__mge_options.copy()
 		if not 'seed' in options:
 			options['seed'] = int(round(time.time() * 1000)) # @todo: may not work correctly for possible parallel usage.
+		options['parallel'] = self.__parallel
 		
 		# Select the GED algorithm.
 		self.__mge.set_options(mge_options_to_string(options))
@@ -676,8 +677,11 @@ class MedianPreimageGenerator(PreimageGenerator):
 					  edge_labels=self._dataset.edge_labels, 
 					  node_attrs=self._dataset.node_attrs, 
 					  edge_attrs=self._dataset.edge_attrs)
-		self.__mge.set_init_method(self.__ged_options['method'], ged_options_to_string(self.__ged_options))
-		self.__mge.set_descent_method(self.__ged_options['method'], ged_options_to_string(self.__ged_options))
+		ged_options = self.__ged_options.copy()
+		if self.__parallel:
+			ged_options['threads'] = 1
+		self.__mge.set_init_method(self.__ged_options['method'], ged_options_to_string(ged_options))
+		self.__mge.set_descent_method(self.__ged_options['method'], ged_options_to_string(ged_options))
 		
 		# Run the estimator.
 		self.__mge.run(graph_ids, set_median_id, gen_median_id)